Whether you celebrate it as a holy day, holiday, or just a day off, Christmas is one of the most anticipated days of the year. ‘Tis the season for traditions, including seasonal blog posts, such as The Twelve (Data) Days of Christmas by Alan D. Duncan, A Christmas Data Carol by Nicola Askham, and an exposé OMG: Santa is Fake by North Pole investigative blogger Henrik Liliendahl Sørensen.

Another Christmas tradition is the exchange of gifts, preceded by a long shopping season, during which in the United States the average person spends $750. The gifts are often wrapped and placed under a tree, concealing their contents until the paper, ribbons, and bows, a presentation which may have taken a long time to prepare, are ripped off in seconds in an exciting rush to see what’s inside. At that point, expectation meets reality and one of two things happen. Either the gift-recipient is thrilled to see a gift they asked for, or they are so disappointed that they can’t possibly imagine the misunderstanding that lead the gift-giver to believe that this so-called gift is what they actually wanted.

This, like most things, reminds me of data quality. So much time, effort, and money is expended creating data-driven deliverables that carry a high expectation of quality for which users believe they have clearly communicated their requirements. Everyone expects the quality of their data to be nice and not naughty. In fact, some people refuse to believe that their data could ever be naughty—until poor-quality data hits them like a stocking full of coal. To paraphrase a line from one of my favorite Christmas songs Grandma Got Run Over by a Reindeer: “you can say that there’s no such thing as naughty data, but as for me and my fellow data quality professionals, we believe.”